Okuno Hirotsugu, Yagi Tetsuya
Division of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
Neural Netw. 2008 Dec;21(10):1431-8. doi: 10.1016/j.neunet.2008.10.003. Epub 2008 Nov 1.
We have designed a visually guided collision warning system with a neuromorphic architecture, employing an algorithm inspired by the visual nervous system of locusts. The system was implemented with mixed analog-digital integrated circuits consisting of an analog resistive network and field-programmable gate array (FPGA) circuits. The resistive network processes the interaction between the laterally spreading excitatory and inhibitory signals instantaneously, which is essential for real-time computation of collision avoidance with a low power consumption and a compact hardware. The system responded selectively to approaching objects of simulated movie images at close range. The system was, however, confronted with serious noise problems due to the vibratory ego-motion, when it was installed in a mobile miniature car. To overcome this problem, we developed the algorithm, which is also installable in FPGA circuits, in order for the system to respond robustly during the ego-motion.
